Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5 papers of 11,817Sort Recent · Most cited
  1. 2022
    BioSLAM: A Bioinspired Lifelong Memory System for General Place RecognitionPeng Yin, Abulikemu Abuduweili, Shiqi Zhao … Sebastian SchererIEEE Transactions · City University of Hong Kong · Carnegie Mellon University
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  2. 2022
    Federated Continual Learning for Socially Aware RoboticsLuke Guerdan, Hatice GüneşIEEE International Conference on Robot and Human Interact… · Carnegie Mellon University · University of Cambridge
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  3. 2022
    Updating Only Encoders Prevents Catastrophic Forgetting of End-to-End ASR ModelsYuki Takashima, Shota Horiguchi, Shinji Watanabe … Yohei KawaguchiInterspeech · Hitachi (Japan) · Johns Hopkins University · +1
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  4. 2022
    CGC: Contrastive Graph Clustering forCommunity Detection and TrackingNamyong Park, Ryan A. Rossi, Eunyee Koh … Christos FaloutsosACM Web Conference 2022 · Carnegie Mellon University · Adobe Systems (United States) · +1
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  5. 2022
    Understanding Collapse in Non-contrastive Siamese Representation LearningAlexander C. Li, Alexei A. Efros, Deepak PathakSpringer LNCS · Carnegie Mellon University · University of California, Berkeley
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About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.